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Olfa Nasraoui Knowledge Discovery & Web Mining Lab

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1 Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments
Olfa Nasraoui Knowledge Discovery & Web Mining Lab Dept of Computer Engineering & Computer Sciences University of Louisville URL: Supported by US National Science Foundation Career Award IIS Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

2 Compressed Vita Endowed Chair of E-commerce in the Department of Computer Engineering & Computer Science at the University of Louisville Director of the Knowledge Discovery and Web Mining Lab at the University of Louisville. Research activities include Data Mining, Web mining, Web Personalization, and Computational Intelligence (Applications of evolutionary computation and fuzzy set theory). Served as program co-chair for several conferences & workshops, including WebKDD 2004, 2005, and 2006 workshops on Web Mining and Web Usage Analysis, held in conjunction with ACM SIGKDD International Conferences on Knowledge Discovery and Data Mining (KDD). Recipient of US National Science Foundation CAREER Award. What I will speak about today is mainly the research products and lessons from a 5-year US National Science Foundation project Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

3 My Collaborative Network?
Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

4 Team: Knowledge Discovery & Web Mining Lab University of Louisville
Director: Olfa Nasraoui (speaker) Current Student Researchers (alphabetically listed): Jeff Cerwinske, Nurcan Durak, Carlos Rojas, Esin Saka, Zhiyong Zhang, Leyla Zhuhadar Note: Gender balanced & multicultural ;-) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

5 Past and Present Collaborators
Raghu Krishnapuram, IBM Research Anupam Joshi, University of Maryland, Baltimore County Hichem Frigui, University of Louisville Hyoil Han, Drexel University Antonio Badia, University of Louisville Roberta Johnson, University Corporation for Atmospheric Research (UCAR) Fabio Gonzalez, Nacional University of Colombia Cesar Cardona, Magnify, Inc. Elizabeth Leon, Nacional University of Colombia Jonatan Gomez, Nacional University of Colombia Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

6 Introduction Information overload: too much information to sift/browse through in order to find desired information Most information on Web is actually irrelevant to a particular user This is what motivated interest in techniques for Web personalization As they surf a website, users leave a wealth of historic data about what pages they have viewed, choices they have made, etc Web Usage Mining: A branch of Web Mining (itself a branch of data mining) that aims to discover interesting patterns from Web usage data (typically Web Log data/clickstreams) (Yan et al. 1996, Cooley et al. 1997, Shahabi, 1997; Zaiane et al. 1998, Spiliopoulou & Faulstich, 1999, Nasraoui et al. 1999, Borges & Levene, 1999, Srivastava et al. 2000, Mobasher et al. 2000; Eirinaki & Vazirgiannis, 2003) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

7 Introduction Web Personalization: Aims to adapt the Website according to the user’s activity or interests (Perkowitz & Etzioni, 1997, Breeze et al. 1998, Pazzani, 1999, Schafer et al. 1999, Mulvenna, 2000; Mobasher et al. 2001, Burke. 2002, Joachims, 2002; Adomavicius &. Tuzhilin, 2005) Intelligent Web Personalization: often relies on Web Usage Mining (for user modeling) Recommender Systems: recommend items of interest to the users depending on their interest (Adomavicius & Tuzhilin, 2005) Content-based filtering: recommend items similar to the items liked by current user (Balabanovic & Shoham, 1997) No notion of community of users (specialize only to one user) Collaborative filtering: recommend items liked by “similar” users (Konstan et al., 1997; Sarwar et al., 1998; Schafer, 1999) Combine history of a community of users: explicit (ratings) or implicit (clickstreams) Hybrids: combine above (and others) Focus of our research Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

8 Some Challenges in WUM and Personalization
Ambiguity: the level at which clicks are analyzed (URL A, B, or C as basic identifier) is very shallow, almost no meaning Dynamic URLs: meaningless URLs  even more ambiguity Semantic Web Usage Mining: (Oberle et al., 2003) Scalability: Massive Web Log data that cannot fit in main memory requires techniques that are scalable (stream data mining) (Nasraoui et al.: WebKDD 2003, ICDM 2003) Handling Evolution: Usage data that changes with time Mining & Validation in dynamic environments: largely unexplored area…except in: (Mitchell et al. 1994; Widmer, 1996; Maloof & Michalski, 2000) In the Web usage domain: (Desikan & Srivastava, 2004; Nasraoui et al.: WebKDD 2003, ICDM 2003, KDD 2005, Computer Networks 2006, CIKM 2006) From Clicks to Concepts: few efforts exist based on laborious manual construction of concepts, website ontology or taxonomy How to do this automatically? (Berendt et al., 2002; Oberle et al., 2003; Dai & Mobasher, 2002; Eirinaki et al., 2003) Implementing recommender systems can be slow, costly and a bottle neck especially for researchers who need to perform tests on a variety of websites For website owners that cannot afford expensive or complicated solutions Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

9 Different Steps Of our Web Personalization System
STEP 1: OFFLINE PROFILE DISCOVERY STEP 2: ACTIVE RECOMMENDATION User profiles/ User Model Post Processing / Derivation of User Profiles Site Files Recommendation Engine Preprocessing Recommendations Active Session Data Mining: Transaction Clustering Association Rule Discovery Pattern Discovery Server Logs User Sessions Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

10 Challenges & Questions in Web Usage Mining
STEP 1: OFFLINE PROFILE DISCOVERY User profiles/ User Model ACTIVE RECOMMENDATION Post Processing / Derivation of User Profiles Site Files Recommendation Engine Preprocessing Recommendations Active Session Data Mining: Transaction Clustering Association Rule Discovery Pattern Discovery Server Logs User Sessions Dealing with Ambiguity: Semantics? Implicit taxonomy? (Nasraoui, Krishnapuram, Joshi. 1999) Website hierarchy (can help disambiguation, but limited) Explicit taxonomy? (Nasraoui, Soliman, Badia, 2005) From DB associated w/ dynamic URLs Content taxonomy or ontology (can help disambiguation, powerful) Concept hierarchy generalization / URL compression / concept abstraction: (Saka & Nasraoui, 2006) How does abstraction affect quality of user models? Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

11 Challenges & Questions in Web Usage Mining
STEP 1: OFFLINE PROFILE DISCOVERY User profiles/ User Model ACTIVE RECOMMENDATION Post Processing / Derivation of User Profiles Site Files Recommendation Engine Preprocessing Recommendations Active Session Data Mining: Transaction Clustering Association Rule Discovery Pattern Discovery Server Logs User Sessions User Profile Post-processing Criteria? (Saka & Nasraoui, 2006) Aggregated profiles (frequency average)? Robust profiles (discount noise data)? How do they really perform? How to validate? (Nasraoui & Goswami, SDM 2006) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

12 Challenges & Questions in Web Usage Mining
STEP 1: OFFLINE PROFILE DISCOVERY User profiles/ User Model ACTIVE RECOMMENDATION Post Processing / Derivation of User Profiles Site Files Recommendation Engine Preprocessing Recommendations Active Session Data Mining: Transaction Clustering Association Rule Discovery Pattern Discovery Server Logs User Sessions Evolution: (Nasraoui, Cerwinske, Rojas, Gonzalez. CIKM 2006) Detecting & characterizing profile evolution & change? Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

13 Challenges & Questions in Web Personalization
STEP 1: OFFLINE PROFILE DISCOVERY User profiles/ User Model ACTIVE RECOMMENDATION Post Processing / Derivation of User Profiles Site Files Recommendation Engine Preprocessing Recommendations Active Session Data Mining: Transaction Clustering Association Rule Discovery Pattern Discovery Server Logs User Sessions In case of massive evolving data streams: Need stream data mining (Nasraoui et al. ICDM’03, WebKDD 2003) Need stream-based recommender systems? (Nasraoui et al. CIKM 2006) How do stream-based recommender systems perform under evolution? How to validate above? (Nasraoui et al. CIKM 2006) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

14 Challenges & Questions in Web Personalization
STEP 1: OFFLINE PROFILE DISCOVERY User profiles/ User Model ACTIVE RECOMMENDATION Post Processing / Derivation of User Profiles Site Files Recommendation Engine Preprocessing Recommendations Active Session Data Mining: Transaction Clustering Association Rule Discovery Pattern Discovery Server Logs User Sessions Implementing Recommender Systems: Fast, easy, scalable, cheap, free? At least to help support research… But Grand advantage: help the little guy… (Nasraoui, Zhang, Saka, SIGIR-OSIR 2006) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

15 What’s in a click? Access log: Record of URLs accessed on Website
Web Usage Mining: - Ambiguity: - Implicit Semantics: website hierarchy - Explicit Semantics: DB w/ taxonomy of dynamic URLs - What is effect of generalization / URL compression / concept abstraction - Noise: - Detecting and characterizing evolution in dynamic environments -Recommender Systems in dynamic environments - Fast, Easy, Free Implementation - Mining Conceptual Web Clickstreams What’s in a click? Access log: Record of URLs accessed on Website Log entry: access date, time, IP address, URL viewed, …etc. Modeling User Sessions: set of clicks, pages, URLs (Cooley et al. 1997) Map URLs on site to indices User session vector s(i): temporally compact sequence of Web accesses by a user (consecutive requests within time threshold: e.g. 45 minutes) URLs: Orthogonal? (Traditional approach) Exploit some implicit concept hierarchy: website hierarchy (easy to infer from URLs) (Nasraoui, Krishnapuram, Joshi. 1999) Dynamic URLs: Exploit some explicit concept hierarchy: encoded in Web item database (Nasraoui, Soliman, Badia, 2005) How to take above into account? Integrate into the similarity measure while clustering Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

16 Similarity Measure (Nasraoui, Krishnapuram, Joshi. 1999)
Map NU URLs on site to indices User session vector s(i) : temporally compact sequence of Web accesses by a user If site structure ignored cosine similarity Taking site structure into account  relate distinct URLs: pi: path from root to ith URL’s node O. Nasraoui and R. Krishnapuram, and A. Joshi. Mining Web Access Logs Using a Relational Clustering Algorithm Based on a Robust Estimator, 8th International World Wide Web Conference, Toronto, pp , 1999. Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

17 Web Session Similarity Measure: variant of cosine that takes into account item relatedness
Taking site structure into account: Final Web Session Similarity = Concept Hierarchies: helpful in many data mining contexts: (E.g. in association rule mining: Srikant &. Agrawal, 1995, in text: Chakrabarti et al., 1997, in Web usage mining: Berendt, 2001, Eirinaki, 2003) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

18 Role of Similarity Measure: Adding semantics
Web Usage Mining: - Ambiguity: - Implicit Semantics: website hierarchy - Explicit Semantics: DB w/ taxonomy of dynamic URLs - What is effect of generalization / URL compression / concept abstraction - Noise: - Detecting and characterizing evolution in dynamic environments - Recommender Systems in dynamic environments - Implementation - Mining Conceptual Web Clickstreams Role of Similarity Measure: Adding semantics Problem: Dynamic URLs, such as universal.aspx?id=56: hard to recognize based only on their URL  affects presentation & interpretation of discovered user profiles! hard to relate (among each other) based only on their URL  affects Web usage mining! Solution: Use available external data that maps dynamic URLs to hierarchically related and more meaningful descriptions Explicit taxonomy: parent item  child item transform URL into regular looking URL: parent/child/grand-child…etc handle this URL using previous implicit website hierarchy approach: inferred by tokenizing the URL string Ultimately, both implicit and explicit taxonomy information are seamlessly incorporated into the data mining algorithm (clustering) via the Web session similarity measure Olfa Nasraoui, Maha Soliman, and Antonio Badia, Mining Evolving User Profiles and More – A Real Life Case Study, In Proc. Data Mining meets Marketing workshop, New York, NY, 2005. Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

19 Mapping Dynamic URLs to Semantic URLs (Nasraoui, Soliman, Badia, 2005)
Problem: Dynamic URLs, such as universal.aspx?id=56, are hard to recognize based only on their URL  affects presentation of profiles! hard to relate (among each other) based only on their URL  affects Web usage mining!. Solution: We resorted to available external data, provided by the website designers, that maps dynamic URLs to hierarchically related and more meaningful descriptions. Taxonomy Data Provided by the website designers menus_id item_name item_level parent_item sequence Resource/url 56 Regulations and Laws 1 4939 universal.aspx NST Center® Nst Example: Dynamic URL: universal.aspx?id=56  Semantic URL: NST Center® / Regulations and Laws Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

20 Mapping Dynamic URLs to Semantic URLs (another example)
universal.aspx?id=6770  ? since item #6770 has as parent: item #56 menus_id item_name item_ level parent_item sequence url 6770 Air Quality and Emission Standards 2 56 1 universal.aspx Recall: Item 56 = (NST Center® / Regulations and Laws ) Hence, universal.aspx?id=6770  NST Center® / Regulations and Laws / Air Quality and Emission Standards Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

21 Concept Generalization/Abstraction
Generalize lower/specific concepts to higher concepts Mechanism: IF Sim (URLi, URLj) > Threshold THEN merge URLs Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

22 Concept Generalization/Abstraction
Generalize lower/specific concepts to higher concepts Mechanism: IF Sim (URLi, URLj) > Threshold THEN merge URLs Effects: Helps in disambiguation URL compression Easily reach compression rates in 80% range depending on merging threshold Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

23 Concept Generalization/Abstraction
Generalize lower/specific concepts to higher concepts Mechanism: IF Sim (URLi, URLj) > Threshold THEN merge URLs Effects: Helps in disambiguation URL compression Easily reach compression rates in 90% range depending on merging threshold Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

24 Aggressive Concept Generalization/Abstraction
Generalize even more lower/specific concepts to higher concepts Mechanism: IF Sim (URLi, URLj) > Even-bigger-Threshold THEN merge URLs More drastic effects: Helps in disambiguation URL compression Easily reach compression rates in 90% range depending on merging threshold Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

25 Effect of Compression σi First, the mining + validation methodology:
Web Usage Mining: - Ambiguity: - Implicit Semantics: website hierarchy - Explicit Semantics: DB w/ taxonomy of dynamic URLs - What is effect of generalization / URL compression / concept abstraction - Noise: - Detecting and characterizing evolution in dynamic environments - Recommender Systems in dynamic environments - Implementation - Mining Conceptual Web Clickstreams Effect of Compression First, the mining + validation methodology: Perform Web Usage Mining: Pre-process Web log data (includes URL transformations taking into account implicit or explicit concept hierarchy) Cluster user sessions into optimal number of user profiles using HUNC (Hierarchical Unsupervised Niche Clustering) Localized Error-Tolerant profiles maximize a measure of soft transaction support with dynamically optimized error-tolerance  Optional Post-processing: (Later…) Frequency Averaging: compute frequency of each URL in each cluster  profile Robust Profiles: ignore noisy user sessions when computing the above Validate discovered profiles against Web sessions σi Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

26 Validation in an Information Retrieval Context (Nasraoui & Goswami, 2005)
Profiles are patterns that summarize the input transaction data Quality of discovered profiles as a summary of the input transactions: Precision (the profile’s items are all correct or included in an original input transaction/session, i.e. no extra items) Coverage/recall (a profile’s items are complete compared to an transaction or session, i.e. no missed items) Interestingness measure: Given , define When Qij = Covij, we call Q the Cumulative Coverage of Transactions, and it answers the Question Is the data set completely summarized/represented by the mined profiles? . When Qij = Precij, we call Q the Cumulative Precision of Transactions, and it answers the Question: Is the data set faithfully/accurately summarized/represented by the mined profiles? These measures quantify the quality of mined profiles from the point of view of providing an accurate summary of the input data. Note: Qi = Probability {Precision ≥ Qmin} or Probability {Coverage ≥ Qmin} Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

27 Precision Quality Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

28 Coverage Quality Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

29 Observations Compression decreases Quality (as expected …)
However, level of compression (or abstraction) is not an important factor What seemed to matter most is “whether” any compression is made or not? Compression  distortion of original data (hence reduced quality) But let’s not forget… Compression  reduced sparsity of the session matrix (hence may help clustering results) Compression  drastic reduction in # items (hence speed up the mining…) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

30 Web Usage Mining: - Ambiguity: - Implicit Semantics: - Explicit Semantics: - What is effect of generalization / URL compression? - Noise: Effect of post-processing: - Robust profiles - Frequency averaging - Detecting and characterizing evolution in dynamic environments - Recommender Systems in dynamic environments - Recommender implementation - Mining Conceptual Web Clickstreams Handling Noise: Effect of Robustifying the Profiles (Nasraoui & Krishnapuram, SDM 2002) Perform Web Usage Mining: Pre-process Web log data (includes URL transformations taking into account implicit or explicit concept hierarchy) Cluster user sessions into optimal number of user profiles using HUNC (Hierarchical Unsupervised Niche Clustering) Localized Error-Tolerant profiles maximize a measure of soft transaction support with dynamically optimized error-tolerance  Post-process profiles: Simple Means: Compute (URL-frequency) means/centroids for each cluster Robust Means: Robust weight of a session into a profile (varies between 0 and 1): wij = e(-(1-Simij)2/ i ) user sessions with wij < wmin are ignored when averaging the URL frequencies in their cluster Validate discovered profiles against Web sessions σi Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

31 Precision Quality for various robustness levels wmin
No post-processing (raw profiles) Post-processing: various robustness levels Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

32 Coverage Quality for various robustness levels wmin
Post-processing: Optimal robustness level (0.2) No post-processing (raw profiles) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

33 F1 Quality for various robustness levels wmin
No post-processing (raw profiles) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

34 Observations Post-processing decreases Precision
However, it improves coverage Computing the URL frequency means of all sessions in each profile/cluster brings up to the surface some URLs that did not make it through the optimization process resulting in the raw profiles More URLs improve coverage, however, hurt precision Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

35 Tracking Evolving Profiles (Nasraoui, Soliman, Badia, 2005)
Web Usage Mining: - Ambiguity: - Implicit Semantics: - Explicit Semantics: - What is effect of generalization / URL compression? - Noise: Effect of post-processing: - Robust profiles - Frequency averaging - Detecting and characterizing evolution in dynamic environments - Recommender Systems in dynamic environments - Recommender Implementation - Mining Conceptual Web Clickstreams Tracking Evolving Profiles (Nasraoui, Soliman, Badia, 2005) Mine user sessions in several batches (for each period) Automated comparison between new profiles and all the old profiles discovered in previous batches. Each profile pi is discovered along with an automatically determined measure of scale σi  boundary around each profile This allows us to automatically determine whether two profiles are compatible based on their distance compared to their respective boundaries σi σ1 σ2 p2 p1 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

36 Tracking Evolving Access Patterns
Four events can be detected from the comparison: Persistence: New profiles are compatible with the old profiles. Birth: New profiles are incompatible with any previous profile. Death: Old profile finds no compatible profile from the new batch. Atavism: Old profile that disappears, and then reappears (i.e. via compatibility) again in a later batch Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

37 Profile Events Birth Persistence profile Atavism time
Atavism Persistence Birth Birth Persistence profile Atavism time Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

38 Tracking Evolving Access Patterns: Example of Atavism
This profile reappears again in last 2 weeks of August The same profile disappears in first 2 weeks of August Here is one profile in June June 04 July 04 /Write_Up_Display.Aspx/Navy Community/Navy/ApprovedInteriorCoatings&Write_Ups_Id/BilgePreservation /Write_Up_Display.Aspx/Navy Community/Navy/Approved InteriorCoatings&Write_Ups_Id/Ballast Tanks Aug 04-Part1 Aug 04-Part2 /Write_Up_Display.Aspx/NavyCommunity/Navy/Approved Interior Coatings&Write_Ups_Id/Bilge Preservation /Write_Up_Display.Aspx/Write_Ups_Id/Project: MSC Non-Skid /Write_Up_Display.Aspx/NavyCommunity/Navy/Approval Of Coatings For Navy Use (Roadmap) Sep 04 Write_Up_Display.Aspx/NavyCommunity/Navy/Approved Interior Coatings&Write_Ups_Id/Bilge Preservation /Write_Up_Display.Aspx/NavyCommunity/Navy/Approved Exterior Coatings&Write_Ups_Id/Non-Skid Walk Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

39 Why track Evolving Profiles?
Form long term evolution patterns for interesting profiles Predict seasonality Support marketing efforts (if marketing campaigns are performed during these periods) Forecast profile re-emergence to improve downstream personalization process via a caching process Frequent atavism  profile should be cached Help improve scalability of Web usage mining algorithm Process Web usage data in batches Integrate tracking evolving profiles within mining algorithm Maintain previously discovered profiles Eliminate a majority of the new sessions from analysis (if similar to existing profiles) Focus on typically smaller data consisting of sessions from truly emerging user profiles Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

40 Recommender Systems in Dynamic Usage Environments
Web Usage Mining: - Ambiguity: - Implicit Semantics: - Explicit Semantics: - What is effect of generalization / URL compression? - Noise: Effect of post-processing: - Robust profiles - Frequency averaging - Detecting and characterizing evolution in dynamic environments - Recommender Systems in dynamic environments - Recommender Implementation - Mining Conceptual Web Clickstreams Recommender Systems in Dynamic Usage Environments For massive Data streams, must use a stream mining framework Furthermore must be able to continuously mine evolving data streams TECNO-Streams: Tracking Evolving Clusters in Noisy Streams Inspired by the immune system Immune system: interaction between external agents (antigens) and immune memory (B-cells) Artificial immune system: Antigens = data stream B-cells = cluster/profile stream synopsis = evolving memory B-cells have an age (since their creation) Gradual forgetting of older B-cells B-cells compete to survive by cloning multiple copies of themselves Cloning is proportional to the B-cell stimulation B-cell stimulation: defined as density criterion of data around a profile (this is what is being optimized!) O. Nasraoui, C. Cardona, C. Rojas, and F. Gonzalez. Mining Evolving User Profiles in Noisy Web Clickstream Data with a Scalable Immune System Clustering Algorithm, in Proc. of WebKDD 2003, Washington DC, Aug. 2003, Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

41 The Immune Network  Memory
External antigen (RED) stimulates binding B-cell  B-cell (GREEN) clones copies of itself (PINK) Stimulation breeds Survival Even after external antigen disappears: B-cells co-stimulate each other  thus sustaining each other  Memory! Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

42 General Architecture of TECNO-Streams Approach
1-Pass Adaptive Immune Learning Evolving data  Immune network information system Stimulation (competition & memory) Age (old vs. new) Outliers (based on activation) ? Evolving Immune Network (compressed into subnetworks) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

43 No Outlier? Yes No Trap Initial Data Initialize ImmuNet and MaxLimit
Compress ImmuNet into K subNet’s Memory Constraints Present NEW antigen data Identify nearest subNet* Compute soft activations in subNet* Start/ Reset Yes Activates ImmuNet? Update subNet* B-cells’ Influence range /scale No Update subNet* B-cells’ stimulations Clone antigen Clone and Mutate B-cells Outlier? Domain Knowledge Constraints Kill lethal B-cells Kill extra B-cells (based on age/stimulation strategy) , increase acuteness of competition OR Move oldest patterns to aux. storage Yes #B-cells > MaxLimit? Secondary storage No ImmuNet Stat’s & Visualization Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments Compress ImmuNet

44 Adherence to Requirements for Clustering Data Streams (Barbara’ 02)
Compactness of representation Network of B-cells: each cell can recognize several antigens B-cells compressed into clusters/sub-networks Fast incremental processing of new data points New antigen influences only activated sub-network Activated cells updated incrementally Proposed approach learns in 1 pass. Clear and fast identification of “outliers” New antigen that does not activate any subnetwork is a potential outlier  create new B-cell to recognize it This new B-cell could grow into a subnetwork (if it is stimulated by a new trend) or die/move to disk (if outlier) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

45 Validation Methodology in Dynamic Environments
Limit Working Capacity (memory) for Profile Synopsis in TECNO-Streams (or Instance Base for K-NN) to 30 cells/instances Perform 1 pass mining + validation First present all combination subset(s) of a real ground-truth session to recommender, Determine closest neighborhood of profiles from TECNO-Stream’s synopsis (or instances for KNN) Accumulate URLs in neighborhood Sort and select top N URLs  Recommendations Then Validate against ground-truth/complete session (precision, coverage, F1), Finally present complete session to TECNO-Streams (and K-NN) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

46 Validation Methodology in Dynamic Environments
Scenario D (Drastic changes): We partitioned real Web sessions into 20 distinct sets of sessions, each one assigned to one of 20 previously discovered and validated profiles. Then we presented these sessions to the immune clustering + recommendation + validation algorithm one profile at a time. That is, we first present the sessions assigned to ground truth profile/trend 0, then the sessions assigned to profile 1, …, etc. Scenario M (Mild changes): present Web sessions in chronological order exactly as they were received in real time by the web server Scenario (Repeating Drastic changes): Same as Scenario D, but presented profiles 1,2,3,4,5,1,2,3,4,5 (Repetition). Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

47 Dendogram of the 20 profile (vectors) 1.7K sessions, 343 URLs
Memory capacity limited to 30 nodes in TECNO-Streams’ synopsis, 30 KNN-instances Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

48 Ramp-up: both deteriorate equally as environment changes
Drastic Changes: F1 versus session number (vertical lines: environment changes),1.7K sessions Ramp-up: both deteriorate equally as environment changes - With sustained environment, KNN climbs higher (intense memorization of immediate past) - On the other hand TECNO-Streams forms a “compressed” summary via optimization  lossy compression Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

49 Mild Changes: F1 versus session number, 1.7K sessions
TECNO-Streams higher (noisy, naturally occurring but unexpected fluctuation call for more intelligent optimization?) The real challenge is that here, ALL 20 usage trends are presented simultaneously as opposed to one at a time (scenario M)! Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

50 However, the 2nd time that a past environment re-occurs:
Repeating Drastic Changes: F1 versus session number (vertical lines: environment changes), 1.7K sessions KNN higher (same as drastic: intense memorization of immediate past) However, the 2nd time that a past environment re-occurs: TECNO-Stream’s performance improves significantly compared to the 1st time (longer term memory, 2ndary immune response known to be stronger) - KNN’s performance remains identical to the 1st time (deterministic) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

51 Dendogram of the 93 profile (vectors) Bigger Data Set (~30K sessions, 30K URLs):
Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

52 Memory capacity limited to 150 nodes in TECNO-Streams’ synopsis, 150 KNN-instances
Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

53 Ramp-up: both deteriorate equally as environment changes
Bigger Data Set (~30K sessions, ~18K items): Drastic Changes: F1 versus session number (vertical lines: environment changes) Ramp-up: both deteriorate equally as environment changes Either one of KNN or TECNO-Streams seem to perform better depending on profile Overall, both recommenders’ performances are very poor for “some” usage trends!!! (Note the dimensionality and sparsity is much higher for the big data!) These trends are contaminated by too many noise sessions (close to 50%)! Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

54 KNN-Streams slightly higher ?
Bigger Data Set (~30K sessions): Mild Changes: F1 versus session number KNN-Streams slightly higher ? Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

55 However, the 2nd time that a past environment re-occurs:
Bigger Data Set (~30K sessions): Repeating Drastic Changes: F1 versus session number (vertical lines: environment changes) KNN slightly higher (same as drastic: intense memorization of immediate past) However, the 2nd time that a past environment re-occurs: TECNO-Stream’s performance improves slightly compared to the 1st time (longer term memory, 2ndary immune response known to be stronger) - KNN’s performance remains identical to the 1st time (deterministic) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

56 Memory capacity limited to 500 nodes in TECNO-Streams’ synopsis, 500 KNN-instances
Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

57 Ramp-up: both deteriorate equally as environment changes
Bigger Data Set (~30K sessions): Drastic Changes: F1 versus session number (vertical lines: environment changes) Ramp-up: both deteriorate equally as environment changes Either one of KNN or TECNO-Streams seem to perform better depending on profile Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

58 Bigger Data Set (~30K sessions): Mild Changes: F1 versus session number
KNN-Streams slightly higher ? But overall both are poor Possibly because of extremely high dimensionality (>17000) and sparsity! which wrecks havoc on Collaborative filtering in streaming environments!!! Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

59 Bigger Data Set (~30K sessions): Repeating Drastic Changes: F1 versus session number (vertical lines: environment changes) Either one of KNN or TECNO-Streams seem to perform better depending on profile Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

60 Personalization: Implementation Issues
Web Usage Mining: - Ambiguity: - Implicit Semantics: - Explicit Semantics: - What is effect of generalization / URL compression? - Noise: Effect of post-processing: - Robust profiles - Frequency averaging - Detecting and characterizing evolution in dynamic environments - Recommender Systems in dynamic environments - Recommender Implementation - Mining Conceptual Web Clickstreams Personalization: Implementation Issues Fast Easy Scalable Cheap? Free…? Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

61 Summary of Methodology
Systematic framework for a fast and easy implementation and deployment of a recommendation system on one or several affiliated or subject-specific websites based on any available combination of open source tools that include crawling, indexing, and searching capabilities Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

62 Supported Approaches Content based filtering (straight forward)
Collaborative filtering (more complex) Hybrids that combine the power of both (2 types): Cascaded (2 options): First collaborative filtering (obtain collaborative recommendations), then content-based filtering (on previous result) First content-based filtering (obtain content-based set of recommendations), then collaborative filtering (on previous result) Parallel/combined: Perform collaborative filtering on original input Perform content-based filtering on original input Then combine resulting recommendations above by weighting, etc. Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

63 What for? Easily "implement" (existing) recommendation strategies by using a search engine software when it is available, Benefit to research and real life applications by taking advantage of search engines' scalable and built-in indexing and query matching features, instead of implementing a strategy from scratch. Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

64 Advantages to Expect… Multi-Website Integration by Dynamic Linking:
dynamic, personalized, and automated linking of partnering or affiliate websites Crawl several websites + connect through common proxy Giving Control Back to the User or Community instead of the website/business no need for intervention from websites The Open Source Edge Tapping into IR Legacy Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

65 Search Engine 1) Crawling: A crawler retrieves the web pages that are to be included in a searchable collection, 2) Parsing: The crawled documents are parsed to extract the terms that they contain, 3) Indexing: An inverted index is typically built that maps each parsed term to a set of pages where the term is contained, 4) Query matching: Submit input queries in the form of a set of terms to a search engine interface or to a query matching module that compares this query against the existing index, to produce a ranked list of results or web pages. Two open source products that enable a fast and free implementation of Web search, Text search engine library: Lucene, Web search engine: Nutch, built on Lucene Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

66 Lucene D. Cutting and J. Pedersen, Space optimizations for total ranking, RIAO (Computer Assisted IR) 1997 high-performance, full-featured text search engine library written in Java, can support any application that requires full-text search, especially cross-platform. Examples of using Lucene: Inktomi and Wikipedia's search feature powerful features through a simple API, include scalable, high-performance indexing, available as Open Source software under the Apache License Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

67 Lucene’s features ranked searching
various query types: phrase, wildcard, proximity, fuzzy, range, and more fielded searching (e.g., title, author, contents), date-range searching, sorting by any field, multiple-index searching with merged results, allowing simultaneous update and searching All the above  Heaven on Earth! for implementing recommender system Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

68 Nutch http://lucene.apache.org/nutch/: Lucene based Web search
Adds Web specifics to Lucene: crawler, link-graph database, parsers for HTML and other document formats (pdf, ppt, doc, plain text, etc). Document = sequence of Fields . Field values may be stored, indexed, analyzed (to convert to tokens), or vectored. Uses Lucene's index: Inverted Index that maps a term  field ID, and a set of document IDs, with the position within each document. Given a query, Nutch by default searches URLs, anchors, and content of documents Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

69 Proposed Methodology Two requirements for tweaking a search engine to work like a recommender sys. An index: The source of the recommendations must be indexed in a format that is easy to search. A querying mechanism: the input to the recommendation procedure must be transformable into a query Query is expressed in terms of the entities upon which the index is based Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

70 Content-based filtering
Given a few pages that a user has viewed, the system recommends other pages with content that is similar to the content of the viewed pages Step 1: Preliminary Crawling and Indexing of website(s) (done offline): to form content of the recommendations, and then forming a reverse index that maps each keyword to a set of pages in which it is contained. Store the most frequent terms in each document as a vector field, that is indexed and used later in retrieval Step 2: Query Formation and Scoring: transform a new user session into a query that can be submitted to the search engine. Map each URL in user session to a set of content terms (top k frequent terms) using an added package net.nutch.searcher.pageurl. Combine these terms with their frequencies to form a query vector, Submit query to Nutch as a Fielded query (i.e. the query vector is compared to the indexed Web document vector field). Finally, rank results according to cosine similarity with the query vector in the vector space domain modification of the default scoring mechanism of SortComparatorSource in the LuceneQueryOptimizer class (which is part of the package net.nutch.searcher) session  URLS  terms  fielded query vector  results (ranked according to cosine similarity (result vector, query vector)) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

71 Cascaded Hybrids Previous sessions Info 2 User profiles Info 2
Type 1: compares current session to (all) previous sessions Recommendations (items) Collaborative filtering Content-based filtering Collaborative session Previous sessions Info 2 Type 2: compares current session to several user profiles Recommendations (items) Collaborative filtering Content-based filtering Collaborative session User profiles Info 2 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

72 Implementation Crawled web pages in following domains:
.wikipedia.org .ucar.edu .nasa.gov  (this corresponds to Step 1 of content-based filtering) The content was indexed using nutch the nutch search engine application was launched to accept queries (in our case transformed user sessions!) A proxy was set at one port on our server based on the Open Source SQUID Web proxy software (http://www.squid-cache.org/) Additional C code to track each session, convert it to an appropriate query, and submit this query to nutch Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

73 Example Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

74 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

75 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

76 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

77 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

78 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

79 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

80 Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

81 Web Usage Mining: - Ambiguity: - Implicit Semantics: - Explicit Semantics: - What is effect of generalization / URL compression? - Noise: Effect of post-processing: - Robust profiles - Frequency averaging - Detecting and characterizing evolution in dynamic environments - Recommender Systems in dynamic environments - Recommender Implementation - Mining Conceptual Web Clickstreams Conceptual User Session Modeling (w/ lead author: Dr. Hyoil Han, Drexel Univ.) P. Achananuparp, H. Han, O. Nasraoui and R. Johnson, Semantically Enhanced User Modeling, ACM SAC 2007, also in Tech. report No IST TR-06-1, Drexel University, September 2006. Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

82 Windows to the Universe: http://www. windows. ucar
Windows to the Universe: (education & outreach website for NASA, NCAR, and other research agencies/groups) P. Achananuparp, H. Han, O. Nasraoui and R. Johnson, Semantically Enhanced User Modeling, ACM SAC 2007. Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

83 Use Wikipedia categories to get large set of Concept terms (specific to physics, astronomy, earth science, etc) Aberration Accretion Albedo Altitude Andromeda Antimatter Aphelion Apogee Apoapsis Aquatic Mammal Asteroid Astrobiology Astrometry Astronaut Astronomer Astronomy Astrophysics Atmosphere Aurora Big Bang Biosphere Black hole Blizzard Bursters Cenozoic Ceres Chemical Element Chinese Deity Chromosphere Coma Comet Constellation Corona Coronagraph Craters Crustacean Cryosphere Culmination Dark Matter Desert Diety Doppler Effect Doppler Shift Earth Earth's crust Eclipse Ecology Ecosystem Epoch Equinox Eruption Evolution Facula Filament Fireball Flare Forecast Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

84 Use URLs to prune Wikipedia concepts to those that are relevant to user sessions context (in the usage logs) Accretion Astronaut Atmosphere Aurora Biosphere Black hole Crustacean Culmination Earth Eclipse Epoch Eruption Fossil Fusion Geosphere Granulation Halo Heliosphere Ionosphere Island Jupiter Light Lithosphere Mars Mercury Mineral Moon Occultation Phase Planet Polarization Rain Forest Satellite Scientist Star Weather Zodiac Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

85 Map user sessions  term sets (content),  concept sessions
Find most semantically related concept for each term either the exactly matched concept or a more general concept. Use the concept hierarchy in WordNet’s taxonomy calculate a path-based measure between term-concept pairs IF Sim < threshold Then unrelated Evaluation: Compare automatically extracted concepts in 100 sessions with those assigned by Human evaluator (ground truth) using prevision/recall P. Achananuparp, H. Han, O. Nasraoui and R. Johnson, Semantically Enhanced User Modeling, ACM SAC 2007. Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

86 Summary of Talk: Challenges & Proposed Solutions in Web Usage Mining & Personalization
Mining Web Clickstreams  User Profiles / User Models Semantics for disambiguation: Implicitly derived (e.g. from website hierarchy) Explicit (e.g. from related Databases that describe a hierarchy of the items/web pages) Content semantics  Conceptual user model Noise  Robust profiles Scalability: how to scale to massive data streams? need to process data in one pass to mine continuously evolving user profiles + work under very stringent constraints Evolution: Track profiles over periods, Define profile evolution events Recommender Systems (that use the user profiles/models discovered above) Evolution: Validate continuously mined evolving user profiles against evolution scenarios? Implementation: fast, easy, scalable, cheap, free (use existing open source indexing+ search engine software) Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

87 REFERENCES IN WEB USAGE MINING & PERSONALIZATION
Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

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92 [34] R. Srikant, R. Agrawal, Mining Generalized Association Rules, in Proc. of 21st VLDB Conf., Zurich, Switzerland, September 1995. [35] S. Chakrabarti, B. Dom, R. Agrawal, P. Raghavan, Using taxonomy, discriminants, and signatures for navigation in text databases, in Proc. of the 23rd VLDB Conference, Athens, Greece, 1997. [36] B. Berendt, Understanding Web usage at different levels of abstraction: coarsening and visualizing sequences, in Proc. of the Mining Log Data Across All Customer TouchPoints Workshop (WEBKDD‘01), San Francisco, CA, August 2001 [37] Desikan P. and Srivastava J., Mining Temporally Evolving Graphs. In Proceedings of “WebKDD workshop on Web Mining and Web Usage Analysis”, B. Mobasher, B. Liu, B. Masand, O. Nasraoui, Eds. part of the ACM KDD: Knowledge Discovery and Data Mining Conference, Seattle, WA, 2004. [38] Eirinaki M., Vazirgiannis M. Web mining for web personalization. ACM Transactions On Internet Technology (TOIT), 3(1), 1-27, 2003. [39] Joachims T., Optimizing search engines using clickthrough data. In Proc. of the 8th ACM SIGKDD Conference, , 2002. [40] Nasraoui O., Krishnapuram R., Joshi A., and Kamdar T., Automatic Web User Profiling and Personalization using Robust Fuzzy Relational Clustering, in “E-Commerce and Intelligent Methods” in the series “Studies in Fuzziness and Soft Computing”, J. Segovia, P. Szczepaniak, and M. Niedzwiedzinski, Ed, Springer-Verlag, 2002. Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

93 [45] CDNow.com – http://www.cdnow.com
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[57] O. Nasraoui, J. Cerwinske, C. Rojas, and F. Gonzalez, Collaborative Filtering in Dynamic Usage Environments, in Proc. Conference on Information and Knowledge Management: CIKM, Arlington, VA, Nov [58] O. Nasraoui, Z. Zhang, and E. Saka, “Web Recommender System Implementations in Multiple Flavors: Fast and (Care) Free for All”. In Proceedings of the ACM-SIGIR Open Source Information Retrieval workshop, Seattle, WA, Aug [59] Nasraoui O. and Goswami S., “Mining and Validating Localized Frequent Itemsets with Dynamic Tolerance,” in Proc. SIAM conference on Data Mining, Bethesda, MD, Apr [60] O. Nasraoui, C. Cardona, and C. Rojas. “Using Retrieval Measures to Assess Similarity in Mining Dynamic Web Clickstreams”. In Proceedings of ACM KDD: Knowledge Discovery and Data Mining Conference, Chicago, IL, 2005, [61] O. Nasraoui and M. Pavuluri, “Complete this Puzzle: A Connectionist Approach to Accurate Web Recommendations based on a Committee of Predictors”. In Proceedings of “WebKDD workshop on Web Mining and Web Usage Analysis”, B. Mobasher, B. Liu, B. Masand, O. Nasraoui, Eds. part of the ACM KDD: Knowledge Discovery and Data Mining Conference, Seattle, WA, 2004. [62] O. Nasraoui, C. Cardona, and C. Rojas. “Mining of Evolving Web Clickstreams with Explicit Retrieval Similarity Measures”. In Proceedings of “International Web Dynamics Workshop”, International World Wide Web Conference, New York, NY, May Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments

96 [63] Mitchell T. , Caruana R. , Freitag D. , McDermott, J
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97 Thank You! Any questions?
Nasraoui: Web Usage Mining & Personalization in Noisy, Dynamic, and Ambiguous Environments


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